Under various circumstances, it is desirable to analyze an electrical signal comprising a repetitive waveform. In order to analyze the waveform, the waveform must be identified and extracted. One example of extensive efforts to analyze repetitive waveforms in an electrical signal has been in the field of electromyography.
Electromyography (EMG) is an important tool in the diagnosis of diseases of the peripheral nervous system. An EMG signal is recorded from a needle inserted into a specified muscle, and represents the electrical discharge of groups of muscle fibers. Abnormality is estimated by observing the potentials on an oscilloscope screen. Reliability of the technique for the diagnosis of diseases of the peripheral nervous system have been seriously limited by a lack of a method to accurately and quickly quantity features of the EMG. Additionally, extension of the use of the EMG to diagnosis of disorders of the central nervous system has been limited by an ability to accurately measure pattern information by visual estimation. In visual diagnosis, the physician sees potentials that flicker across the oscilloscope screen at 10 to 30 Hz, and listens to the sound on a loud speaker. The results are also highly dependent on the training of the individual performing the examination and subject to bias. This accounts for limitations on the reproducibility and reliability of the test in diagnosis of diseases of the peripheral nervous system. Another significant limitation is the inability of the observer to quantify certain perimeters such as firing rate and pattern, and relationships between firing patterns and recruitment of units. While attempts have been made by more experienced physicians and researchers to extend EMG to the diagnosis of diseases of the central nervous system, an ability to accurately measure appropriate perimeters have prevented realization of this goal.
Previous attempts to apply computer analysis to the EMG signal have been marginally successful because the signal is extremely variable and complex. Recently, new methods based on modelling of processing (computation) by biologic neurons have demonstrated better capabilities than traditional algorithms for analyzing complex signals such as images or voice signals.
A first method for a motor unit quantitation was developed by Buchthal. "Action Potential Parameters in Normal Muscle and Their Dependence on Physical Variables", F. Buchthal, C. Gold, P. Rosenfalck. Acta Physiol Scand, 1954a (32) 200. His method involves the recording of randomly collected motor units on photographic film or paper. The motor units are visually inspected, and duration, amplitude and phases measured and tabulated. After 20 or more units are measured, resulting mean values are compared with normative data collected by Buchthal. This method is extremely time consuming, taking up to an hour for a single muscle. Since most clinical studies involve examining up to a dozen muscles or more, this method is not practical except in a research setting. Again, it is subject to significant bias by the individual selecting and measuring the units.
Several computer assisted methods of MUP quantization have been developed initially as research tools. The computer programs have generally been developed on laboratory mini computers, and after they have been published they have been made available as software packages.
One of the most significant efforts has been by Dorfman and McGill. "Automated Decomposition of the Clinical Electromyogram", K. C. McGill, K. L. Cummins, L. J. Dorfman. IEEE Trans. Biomed. Eng., 32 (7): 470-477, July 1985. This program is called ADEMG (Automated Decomposition of the EMG). The program records the interference pattern at threshold, 10% or 30% of maximal effort. It then filters and differentiates the signal to locate the motor unit spikes. Motor units are isolated and compared by a template matching scheme. Recurrences of the same unit are aligned in the Fourier domain, and averaged. Superimpositions are resolved whenever possible during matching. A final average is then reprocessed to remove adjacent or overlapping units that may be degrading the average. Duration is computed automatically. Firing rate is analyzed, but is used merely to locate missing occurrences of motor units. No further processing is performed. In general, this method identifies waveforms by two characteristics: firing pattern and template matching. One waveform is subtracted from another, and if the difference is close, it is considered the same waveform. The waveforms are then averaged. This method may erroneously resolve polyphasic motor units into discrete components, thus failing in disease states where motor unit phases are increased. While the program tracks slowly varying wave shapes, it has trouble detecting repeated occurrences of the same when that unit is unstable in configuration, as is the case in several disease states. Under these circumstances, ADEMG may erroneously detect the slightly different occurrences as distinct motor units. The ADEMG only averages waveforms to produce a template; there is no training or learning. Furthermore, the algorithm does not classify the waveform.
An additional signal processing method has been by Gevins. "Igorance-based Neural-Network Signal Processing in Brain Research", Alan S. Gevins and Nelson H. Morgan, June 1987. The method outlined in the paper looks at the application of neural-network classifier-directed methods to known neurological waveform detection. The methods include application to contaminant detection for replacement of ad-hoc detectors, and waveform detection for evoked potential estimation.
In the application of contaminants, expert knowledge of contaminant types is represented by training data which have been hand-marked, which is used to train a neural network to distinguish clean from contaminated data. Several neural networks are used, each trained to detect a different type of contaminant. However, the network is manually trained by known patterns and just detects the known patterns occurring in the raw data. The network of this method is incapable of receiving a large number of features and classifying the input on best match; the method only accepts raw data and not features. Furthermore, the method does not disclose any type of initial waveform identification. In the evoked potential estimation, assumptions about the signal and noise properties are necessary based on potential. This method requires preconceived assumptions of the input signal in order to operate.
The prior art requires prior information regarding the data signal in order to process the waveforms. Problems arise when the waveform does not match any of the original assumptions, or there is a superimposition of waveforms. The prior art cannot learn new and unknown waveforms and cannot classify unknown patterns or waveforms.
Most of the prior art utilizes one of the following methods in order to classify the waveform: rule base system, pattern match, or look up table. None of the prior art uses a dynamic architecture which maps the features for classifying the diagnosis.
The following prior art has made attempts to either identify a waveform or classify the waveform, however, none of the prior art is capable of learning an unknown waveform or classifying features by network transformation.
U.S. Pat. No. 4,453,551 issued June 12, 1984 to Anderson et al discloses a pattern recognition assembly for ECG signals for detecting fibrillation. The signals are digitized, stored, and subjected to an AGC routine. Thereafter, samples are subjected to a series of tests to detect an abnormal physiological condition. Various tests which are preformed include determining the amount of zero crossings, ratio of energies contained in the ECG trace, and analyzing the slopes of the ECG signal. The Anderson patent discloses extracting known waveforms and measuring features. The Anderson patent extracts features without looking at the waveform. Anderson does not attempt to identify unknown waveforms but only identifies the presence of known waveforms shapes. Furthermore, the waveform is not extracted.
U.S. Pat. No. 3,858,034, issued Dec. 31, 1974 to Anderson discloses a computer system for detecting QRS complexes of known configurations. The system extracts from the complexes various descriptive features. The system will not detect unknown waveforms nor analyze the entire waveform for classification thereof.
U.S. Pat. No. 3,606,882, issued Sept. 21, 1971 to Zenmon et al discloses a system for diagnosing heart disease which separates the p Waves or the QRS wave from a waveform or cardiac potential. Such a system is representative of typical detectors used in EKG signals which is merely looking for a known waveform and identifies the presence or absence of the waveform.
U.S. Pat. No. 3,587,562, issued June 28, 1971 to Williams discloses a physiological monitoring system which receives physiological signals comprising both respiration and cardiac action. Williams discloses a standard method of recording cardiac and pulmonary signals which is used in any type of physiological system.
U.S. Pat. No. 4,338,950, issued July 13, 1982 in the name of Barlow, Jr. et al discloses a system for sensing and measuring heart beats without the effect of body motion. The Barlow system is a simple detector which identifies the presence of pulse waves from the heart beat. Once a waveform is identified based on a known waveform, the number of waveforms are merely counted. There is no learning nor diagnosis.
U.S. Pat. No. 4,754,762, issued July 5, 1988 to Stuchl discloses an EKG monitoring system which receives the heart muscle signal and isolates the QRS components. The Stuchl reference discloses a typical EKG system wherein the waveform is decomposed and the entire waveform itself is not learned. The system merely detects the presence or absence of a known waveform or it looks for a specific feature.
U.S. Pat. No. 4,770,184, issued Sept. 13, 1988 to Greene, Jr. et al discloses an ultrasonic doppler diagnostic system which utilizes pattern recognition. The Greene discloses a standard fast fourier transform device to obtain a spectrum. The Greene utilizes a known pattern and attempts to diagnose abnormalities on the basis of doppler. Prior knowledge of stored set of known patterns are utilized and the closest match within the data base is used for recognition thereof.
U.S. Pat. No. 4,566,464, issued Jan. 28, 1986 to Piccone et al discloses an epilepsy monitor apparatus for analyzing EEG patterns and an unobtrusive external warning unit to inform the patient of seizure onset. The Piccone reference merely discloses a detector which is designed to recognized the presence or absence of known waveforms. There is no learning nor extraction nor diagnosis. The signal is recorded which is known to be abnormal or epileptic. Then the device differentiates between two types in order to warn.
U.S. Pat. No. 3,902,476, issued Sept. 2, 1975 to Chaumet discloses an apparatus for heart beat rate monitoring. An electrocardiogram signal and the derivative in relation to time of such signals are applied as input with the maximum amplitude of each of the signals stored, and the maximum amplitude of the interference signals are stored so that the crest-to-crest amplitude of the electrocardiogram signal and the crest-to-crest amplitude of the derivative in relation to time of such signals are obtained. This device does not identify or decompose waveforms nor does it take a waveform and extract the waveform from the original signal. The sensed signal must match exactly for identification thereof.
All of the above noted patents are generally detectors which detect either features of a waveform or known waveforms. None of the prior art discloses learning an unknown waveform and classifying the waveform by nonlinear dynamic architecture.